Job Description/Responsibility

In today’s science and technology, the spatial, temporal and property-profile domains are often handled by different academic disciplines. However, real-world systems have spatial extent, temporal dynamics and a variety of physical properties. Modern measurement devices increasingly allow us to link these domains, which can provide us with deeper understanding, better control and new opportunities. However, the rapid increase in the amount of data currently poses a major challenge which requires a corresponding increase in our ability to interpret and make sense out of this “big data”.

Many approaches to handling big data are based on black-box methods which may not be intuitive or transparent for human interpretation. A major focus of Big Data Cybernetics is therefore the development and application of methods which give easily interpretable results, and consequently allow domain experts to play a central role in the data analysis and decision-making processes.

The main goal is to translate “big data” from a large number of sensor channels into “smart data” represented by a combination of theory-driven and data-driven models. The overlap between dynamic subspace identification (from cybernetics) and partial-least-squares modelling (from chemometrics) could for instance be a fruitful common ground for the desired high-dimensional, spatio-temporal modelling. Other types of suitable modelling techniques from physics, statistics, signal processing and machine learning may also be relevant, as long as they are multivariate, open to unexpected events, computationally fast, and their solutions are easy to interpret and validate.

The applicants’ methodological background should include theory and tools for describing scientific knowledge in terms of both first-principles mathematical models as well as data-driven models based on large data sets. It is required to document solid competence in essential areas of automatic control and/or multivariate data modelling. Knowledge in system identification, nonlinear dynamics, feedback control, signal processing, image analysis, visualization or machine learning will be considered an advantage.

Research activities are expected to have a strong international profile and impact, with a long-term perspective and to be concentrated around basic challenges and enabling technologies with relevance and importance for applications and industry.

The department has strong relationships to Norwegian and international industry, with numerous joint research projects including applications in the maritime, offshore, energy, process, aquaculture and medical industries. The research activities of the department rely crucially on external funding, and the development of educational programs may also receive external funding. The successful applicants are expected to engage extensively in applications for external funding, e.g. from the Research Council of Norway, European research and educational agencies, the industry sector, and other available sources.

MSc and PhD candidates from the cybernetics study programs are expected to be competitive in an international job market. The professors/associate professors will play a leading role in developing an educational profile and ensuring an excellent learning environment, in collaboration with colleagues, students and external stakeholders. As such, the professors/associate professors are expected to teach relevant courses at all levels and supervise both MSc and PhD candidates.

The professors/associate professors are also expected to disseminate their research results to a wider audience, as well as participate in the formal management of research, education and other relevant areas of activity in agreement with the department.

Experience Requirements

N/A

Education Requirements

The applicant is required to have a doctoral degree or equivalent in a relevant area as described above, and document solid scientific expertise in essential areas of automatic control/cybernetics/system identification and/or multivariate data modelling/chemometrics/subspace modelling.

For a position as associate professor, the applicant should have a good publication record in terms of papers in peer-reviewed journals and other relevant international publication channels. Documented external funding, experience with research leadership and relevant collaboration with industry will be rated positively. The candidate should have a research potential which makes it likely to qualify for a full professorship within five years of employment, even with normal teaching duties.

For a position as professor, the applicant should demonstrate international experience and have a strong publication record in terms of papers in peer-reviewed journals and other relevant international publication channels. The applicant should document the ability to obtain external funding from relevant sources, be internationally recognized and be able to initiate and lead research at a high international level.

For both position categories, the applicant should demonstrate the ability to develop educational activities and the learning environment. He or she should have experience in the supervision of students or similar experience qualifying for such work.

The applicant should demonstrate communicative skills that qualify for excellent teaching, supervision and dissemination, and have good collaboration skills necessary for joint interdisciplinary projects.

Skills Requirements

CV including information relevant for the qualifications and a full list of publications with bibliographical references

Diplomas and references

The most important publications that are relevant for the evaluation of the applicant’s qualifications (maximum 10 publications)

A brief description of the scientific/technological relevance of the candidate's research

Research proposal for the first five years of employment (maximum 10 pages)

Information about educational experience, including development of study programs, curricula, teaching experience, and development of teaching methods and the learning environment. See “Documentation of an applicant’s pedagogical qualifications”: http://www.ntnu.edu/vacancies/pedagogical-qualifications

Information about dissemination activities

Other documents which the applicant would find relevant

Joint works will also be evaluated. If it is difficult to identify the contributions from individuals in a scientific collaboration, applicants are to enclose a short summary of his/her contribution.

Following the application deadline, a shortlist of applicants will be drawn up, and all applicants will be informed whether they are placed on the shortlist. Shortlisted applicants will be evaluated by an international expert committee. The top candidates from this evaluation will be invited for interviews and trial lectures. The evaluation will take into account not only the accumulated academic production but also the applicant’s potential for scientific development and personal qualities.

Compensation/Benefits

N/A

Apply Instruction

Further details about the position can be obtained from Head of Department Morten Breivik, e-mail: morten.breivik@ntnu.no .

About the Company

Company Name: Norwegian University of Science and Technology

Company Profile: NTNU has the main responsibility for higher education in technology in Norway, and it is the country’s premier institution for the education of engineers. The university offers several programmes of professional study and a broad academic curriculum in the natural sciences, social sciences, teacher education, humanities, medicine and health sciences, economics, finance and administration, as well as architecture and the arts.